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Social norms intervention to promote help-seeking with depressed Asian and European Americans: A pilot study

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Social norms intervention to promote help-seeking with depressed Asian and European Americans: A pilot study

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Content Running Head: Social Norms Intervention to Promote Help-Seeking
Copyright [2022] Crystal Wang

Social Norms Intervention to Promote Help-Seeking with Depressed Asian and European
Americans: A Pilot Study
By
Crystal X. Wang, M.A

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfilment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PSYCHOLOGY)

August 2022

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Acknowledgements
I would like to thank my dissertation chair and faculty mentor, Dr. Stan Huey. To say that
you have changed my life would not be an exaggeration. Not only has your mentorship helped
me grow as a researcher and a scientist, but also as a human being. Your selflessness,
compassion, and unwavering commitment to making the world a better place inspires me to
become a better person. With you as a role model, I have finally found my voice and I am no
longer afraid to stand up for what I believe in and always do the right thing, regardless of the
consequences. I am so honored to have received your mentorship, and I will always look back on
my 6 years in your lab, despite the hardships and sleepless nights, with fondness.
I am also grateful to my incredible committee members, Drs. Steve Lopez, Daphna
Oyserman, Yuri Jang, and Ann Marie Yamada. All four of you have been so generous with
sharing your knowledge and helping me refine my study. This dissertation would not have been
possible without your support and guidance. I want to especially thank Daphna, who helped me
tremendously by reshaping the research design for a more effective intervention. Furthermore, if
I had not taken your fantastic social psychology seminar in my first year, ideas for my second-
year project and dissertation would have never come to fruition, so I am deeply thankful for your
help throughout the years.
I would not have been able to survive graduate school without the love of my dearest
friends in the program. Thank you, Nina Jhaveri, Mariel Bello, Shubir Dutt, and Annemarie
Kellaghan for being the best cohort anyone could ever wish for. We have truly transcended
friendship and become a family over the past 6 years. I also want to thank close graduate school
friends in other areas, especially Cindy Chiang, Valerie Thomas, Peter Wang, Lynn Zhang, and
Maddy Jalbert. You are all so unbelievably precious to me. Even though we are all moving to
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iii
different locations after graduating, you are forever friends, and I hope to always have you in my
life.
Finally, I want to dedicate this dissertation to my family. To my mom and dad: Words
cannot describe how thankful I am to have been born your daughter. You have worked so hard
and made so many sacrifices as immigrants in this strange new country to provide me with the
resources necessary to achieve my dreams. To Michelle: We are so incredibly close that I do not
just consider you my sister, but also my best friend. You are the only person in the world I can
confide in for absolutely everything, and for that I am forever grateful. I love you all so much.

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Table of Contents
Acknowledgements ......................................................................................................................... ii
List of Tables .................................................................................................................................. v
List of Figures ................................................................................................................................ vi
List of Appendices ........................................................................................................................ vii
Abstract ........................................................................................................................................... 1
Introduction ..................................................................................................................................... 3
Chapter 1: Method ........................................................................................................................ 12
Chapter 2: Results ......................................................................................................................... 22
Chapter 3: Discussion ................................................................................................................... 28
References ..................................................................................................................................... 35
Tables ............................................................................................................................................ 48
Figures........................................................................................................................................... 62
Appendix A ................................................................................................................................... 66
Appendix B ................................................................................................................................... 69
Appendix C ................................................................................................................................... 80

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List of Tables
Table 1: Participant characteristics ………………………………….……………………48
Table 2: Aggregate peer norms vs. perceptions of peer norms ……….….………….……50
Table 3: Mental health descriptive information……………………….….………….……51
Table 4: Models of condition effects on mental health outcome trajectories…….….……53
Table 5: Condition x ethnicity interaction models …………………….…....……….……54
Table 6: Condition x interdependence interaction models ………………….……….……55
Table 7: Personal mental health stigma mediation models ………………….……………56
Table 8: Societal mental health stigma mediation models …………………….….………57
Table 9: Condition x ethnicity mediation models (mediator = personal MHS) …….….…58
Table 10: Condition x ethnicity mediation models (mediator = societal MHS) ……...……59
Table 11: Condition x interdependence mediation models (mediator = personal MHS).….60
Table 12: Condition x interdependence mediation models (mediator = societal MHS) …...61

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List of Figures
Figure 1: Figure of participant recruitment, randomization, follow-up, and analysis…….62
Figure 2: Changes in mental health outcome variables over time …………….………….63
Figure 3: Interaction between condition and interdependence on trajectory of mental health
service utilization rates over time ………………………………….…………………………...65
Figure 4: Interaction between condition and interdependence on trajectory of personal
mental health stigma over time ………………………………….……………………………...66

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List of Appendices
Appendix A: Sample Stimuli ………………………………………………………………….67
Appendix B: Measures ………………………………………………………………………...70
Appendix C: Analyses with PNF condition included ……………………...………………….81

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Abstract
Asian Americans underutilize mental health services in comparison to all other ethnic
groups in the United States, and culturally construed mental health stigma (MHS) has been
identified as a primary explanatory factor. Although interventions targeting MHS and help-
seeking are generally efficacious, they lack cross-cultural validity. Due to social norms
sensitivities and greater preference for conformity among East Asians, interventions that target
perceived social norms might be particularly effective with this group. The present study pilots
social norms interventions to target help-seeking in depressed Asian and European Americans
college students with unmet mental health needs. Sixty-two participants who met criteria for
clinical depression were randomly assigned to 1 of 4 conditions: (1) social norms only, (2)
psychoeducation only, (3) social norms + psychoeducation, or (4) placebo control. MHS and
help-seeking attitudes and behaviors were assessed immediately after the intervention and at 2-
week and 3-month follow-up. Due to the low sample size, the social norms + psychoeducation
and social norms only conditions were merged to form a single combined social norms
condition. Across conditions, depressive symptoms decreased and mental health service
utilization increased over time. There were no main effects of intervention condition (i.e.,
psychoeducation vs. combined social norms vs. placebo) on trajectories of MHS or help-seeking;
however, interdependence significantly moderated intervention effects, such that the combined
social norms condition (vs. psychoeducation alone or placebo) led to greater rates of service
utilization and decreased mental health stigma over for individuals with interdependent cultural
construals. Overall, our pilot study suggests that a social norms approach to targeting help-
seeking may be superior to a traditional psychoeducational intervention for those with
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interdependent cultural values, but conclusions are limited due to the small sample size.
Recommendations for future research are discussed.

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Introduction
The economic costs of untreated mental illness in the United States may be as high as
$300 billion a year (NIMH, 2017), which includes estimates of lost productivity and increased
health care costs (Insel, 2008). This immense strain on the economy may be in part due to
significant underutilization of services by distressed Americans (Kessler et al., 2001; Zhou,
2020), which is especially pronounced among Asian Americans. Even when diagnosed with
mental disorders, Asian Americans have the lowest rates of mental health service utilization
among all ethnic groups in the United States (i.e., 18% compared to 46% of Whites; SAMSHA,
2015). Despite research demonstrating the efficacy of mental health interventions for Asian
Americans (Huey & Tilley, 2018), the vast majority of distressed Asian Americans remain
untreated (SAMSHA, 2015). As a result, addressing disparities in service utilization becomes a
pressing societal concern with implications for public health and the economy.
Unmet mental health needs may be especially problematic on college campuses. Rates
and severity of psychopathology across campuses have been steadily increasing every year
(Beiter et al., 2015; Watkins et al., 2011; Xiao et al., 2017), with approximately half of college
students meeting clinical criteria for a mental disorder, and 40% reporting that they felt so
depressed at some point during the past year that it was difficult to function (Hunt & Eisenberg,
2010; Schwartz & Kay, 2009; Zivin et al., 2009). During the COVID-19 pandemic, the “mental
health crisis” across college campuses appeared to worsen, with nearly 40% of college students
reporting current depression and 34% reporting anxiety when surveyed in 2020 (Casey et al.,
2022). Despite the high rates of psychopathology, only 29% of students reported any utilization
of mental health services, and 60% of students reported difficulty accessing care (The Healthy
Minds Study, 2020).
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Of those diagnosed with mental health disorders, service utilization was lowest among
Asian American college students (Hunt & Eisenberg, 2010, Zhou, 2021). Other troubling
research finds that Asian American students may especially be at risk during the COVID-19
pandemic, which created a surge of anti-Asian bias and hate crimes across the globe. Indeed, a
recent study showed that of the Asian American students who experienced COVID-19 related
discrimination, over two-thirds of them met criteria for a mental health disorder (Zhou et al.,
2021). Other research shows that pandemic-related surges in racism contributed to Asian
American college students’ significantly higher rates of mental health distress than European
American students (Hahm et al., 2021; Wu et al., 2021). Despite the high levels of
symptomatology, Asian American students utilized mental health treatment significantly less
than all other college students in the midst of COVID-19 (Zhou, 2021). Because the initial onset
of most lifetime mental disorders will occur before the age of 24 (Kessler et al., 2004), targeting
ethnic and racial disparities in treatment utilization among college students might be of particular
importance, especially in the context of the worsening mental health crisis during the COVID-19
pandemic.
Researchers have identified several barriers to mental health access, and highlighted
mental health stigma (MHS) as a primary factor underlying ethnic disparities in service
utilization (Ihara et al., 2014; Leong & Lau, 2001). MHS is defined as negative cognitions and
reactions towards mental illness endorsed by both the individual and the general public (Corrigan
& Watson, 2002), and is associated with decreased service utilization among college students
(Eisenberg et al., 2009). A meta-analysis found a significant inverse relationship between the two
constructs, such that increased stigma was associated with decreased help-seeking behaviors (d =
-0.27; Clement et al., 2015). Furthermore, a recent study investigating unmet mental health care
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needs in Asian Americans found stigmatizing attitudes to be the most endorsed barrier to service
utilization. Sixty-nine percent of distressed Asian American participants in the study selected
attitudinal barriers as the reason for their unmet mental health needs, versus only 36% for cost
barriers, and 29% for access barriers (Kung et al., 2019). Therefore, utilization disparities may
primarily be a consequence of elevated MHS among individuals of East Asian descent (Masuda
& Boone, 2011; Wang & Huey, 2022).
Indeed, numerous studies have found that MHS is generally higher in Asian Americans
than European Americans (Cheng, 2015; Eisenberg et al., 2009; Ihara et al., 2014; Wang &
Huey, 2022). Because East Asian societies tend to be interdependent in nature and place
emphasis on group harmony (Markus & Kitayama, 1991), mental illness can be perceived as a
deviation from the norm, thereby threatening group harmony (Leong & Lau, 2001;
Papadopoulous, Foster, & Caldwell, 2013). Fearing disapproval or even rejection from in-group
members, distressed interdependent individuals might be more motivated to conceal symptoms
and avoid treatment (Leong & Lau, 2001; Papadopoulous et al., 2013; Wang & Huey, 2022).
Accordingly, traditionally interdependent groups (e.g., Asian, Hispanic, and Greek) are generally
higher in MHS, whereas traditionally independent groups (e.g., European American, German,
and Australian) are generally lower in MHS (Cheng, 2015; Jaques et al., 1973; Papadopoulous et
al., 2013; Papadopoulous et al., 2002; Westbrook et al., 1997). Moreover, cultural priming
research indicates that interdependence may be causally related to mental health stigma among
both Asian and European Americans (Wang & Huey, 2022), suggesting the importance of this
cultural value. As a result, the interdependent nature of East Asian societies should be taken into
account when considering mental health stigma as a barrier to service utilization among Asian
Americans.
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MHS Reduction and Help-seeking Interventions
A number of interventions have been implemented to address mental health stigma
concerns. These MHS reduction interventions employ a wide variety of methods such as
psychoeducation, interpersonal contact, media campaigns, and behavioral therapy, and are
generally effective in reducing stigma (Collins et al., 2013; Dalky, 2012; Morgan et al., 2018; Xu
et al., 2018). However, the designs of most MHS reduction studies are not experimental, and the
lack of comparison conditions makes evaluations of efficacy difficult (Collins et al., 2013). In
addition, these interventions primarily target MHS within the general public, and their effects on
clinical populations are less clear.
Other interventions target help-seeking behaviors and attitudes rather than MHS. A recent
meta-analysis of 98 studies across databases found that the most common help-seeking
interventions consisted of mental health literacy, and had small effects on help-seeking attitudes
and behaviors (Xu et al., 2018). Another meta-analysis specifically investigating randomized
controlled trials for help-seeking found an effect on attitudes, but not behaviors (Gulliver et al.,
2012).
Although there seems to be some evidence for the effectiveness of MHS reduction
interventions, researchers have criticized their limited generalizability to diverse populations
(Collins et al., 2017; Dalky, 2012). MHS interventions targeting ethnic minorities are rare and
often limited by small sample sizes (Dalky, 2012; Xu et al., 2018). Even less common are
interventions that test for differential effects between ethnic or cultural groups (Collins et al.,
2017), and to our knowledge, there has only been one study testing the cross-cultural efficacy of
a MHS reduction intervention (Portocarrero, 2009). In this study, the author compared the effects
of a psychoeducational intervention between Chinese Americans and European Americans, but
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found limited effects with the Chinese participants (Portocarrero, 2009). Specifically, the
Chinese participants’ stigma was only reduced immediately post-treatment, but returned to
baseline 2-weeks later, while the intervention effects endured over time for European Americans.
The author concluded that increasing cultural sensitivity in future interventions might help
improve outcomes with Asian American participants (Portocarrero, 2009).
A Social Norms Approach
One intervention strategy with potential cultural relevance is the social norms approach.
Research suggests that interdependent individuals may be more sensitive to the perceived norms
of a group, perhaps due to values regarding group harmony and adherence to social norms,
whereas those who are higher in independence may be more driven by their own beliefs and
attitudes (Bond & Smith, 1996; Bontempo & Riveros, 1992; Cialdini et al., 2017; Hsu et al.,
2008; Wosinska et al., 1999; Ybarra & Trafimow, 1998). Indeed, members of societies higher in
interdependence are more likely to exert social control, or any form of expressed disapproval
toward deviant behaviors, than those from independent societies (Brauer & Chaurand, 2010).
Moreover, East Asians have a stronger cultural preference towards conformity than European
Americans (Bond & Smith, 1996; Kim & Markus, 1999). Unsurprisingly, greater conformity
motives in Asian American students predict their engagement in problem behavior that they
perceive to be normative among peers, such as heavy drinking, whereas European American
drinking behaviors are more influenced by individualistic motives, such as self-enhancement
(LaBrie et al., 2011).
In terms of mental health utilization, a prior study found that perceived public stigma
(e.g., participants’ perceptions of their peers’ mental health stigma) predicted significant
reductions in help-seeking behaviors, whereas actual public stigma (e.g., the actual rates of
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stigma that exist among peers) had no effect on help-seeking (Kulesza et al., 2015), suggesting
the suitability of social norms interventions for targeting MHS and help-seeking. In addition, a
social norms approach might be especially beneficial for Asian Americans, given evidence that
they report less perceived need for mental health services than other groups (Eisenberg et al.,
2007), and have greater cultural susceptibility to perceived norms and conformity (Bontempo &
Rivero, 1992; Kim & Markus, 1999). As a result, a help-seeking intervention that directly targets
social norms might have cultural relevance for Asian Americans and those high in
interdependence.
The general assumption of the social norms approach is that individuals are often
incorrect in their perceptions of peers’ behaviors and attitudes (Berkowitz, 2002). “Pluralistic
ignorance” describes a situation in which individuals conform to misperceived group norms,
even when they privately reject it (Miller & McFarland, 1991). A frequently documented
example of pluralistic ignorance is with alcohol use among college students, where the majority
of individuals overestimate peers’ approval of heavy drinking and engagement in this behavior,
and therefore drink more in order to match the perceived expectations and behaviors of their
peers (Perkins et al., 1999). Social norms theory posits that correcting these misperceived beliefs
will lead to reductions in problematic behaviors and increased engagement in healthier behaviors
(Berkowitz, 2002). Indeed, social norms interventions have been effective for a wide range of
problem behaviors and attitudes, such as binge-drinking (e.g., Perkins & Berkowitz, 1986;
LaBrie et al., 2013), bullying (Perkins et al., 2011), sexism (e.g., Berkowitz, 2001; Kilmartin et
al., 1999), and racism or discrimination (e.g., Murrar et al., 2020). Although there may be fear of
a “boomerang effect,” or the subsequent increase in problem behaviors from individuals who are
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below the normative mean, studies consistently demonstrate no evidence of iatrogenic effects
from social norms interventions (Prince et al., 2014).
Few studies have directly compared the effectiveness of social norms interventions to
traditional ones. Given the prevalence of pluralistic ignorance, it is likely that receiving
normative information would be superior to educational interventions. Social norms theory posits
that the knowledge of what should be done is often not enough to change problem behaviors
without additional knowledge of what is actually done (Cialdini et al., 1991). For example, most
university students are aware of pro-diversity norms, but they may only increase their
engagement in pro-diversity behaviors if informed that these behaviors are common among their
peers (Murrar et al., 2020). When individuals’ private values conflict with what they perceive as
normative, they tend to conform to the perceived societal norms, regardless of their own personal
beliefs (Murrar et al., 2020; Perkins & Berkowitz, 1986). This may help explain why diversity
workshops and traditional implicit bias trainings often fail to change attitudes long-term and
have no effect on subsequent behaviors (Kaley et al., 2006; Forscher et al., 2019).
Mental health stigma reduction interventions typically provide psychoeducation about
mental illness and the importance of treatment (Dalky, 2012), without including normative
information about attitudes and behaviors that exist in the community (e.g., actual number of
people in need who receive treatment). In contrast, a social norms approach would primarily
target misperceived norms to elicit behavioral change. But because both types of interventions
are generally effective, it is unclear which is the more powerful method to elicit change
(Dempsey et al., 2018). To our knowledge, only one study has directly compared the effects of
educational vs. social norms sun protection intervention; they found no significant difference
between groups on sun protective behaviors, although receiving both interventions was superior
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to either alone (Mahler et al., 2008). Thus, more research is needed to clarify differential effects
of educational vs. social norms interventions, and whether combining the two methods could
increase efficacy. Given research suggesting that individuals typically act in line with perceived
norms rather than their own beliefs, it is expected that a social norms approach will be more
effective in eliciting behavioral change than the traditional psychoeducation approach, but this
has yet to be formally tested in a mental health context.
The Current Study
The current study assessed the separate and combined effects of two mental health
interventions on help-seeking with depressed Asian and European Americans. Participants were
randomly assigned to one of four conditions: (1) social norms only, (2) psychoeducation only,
(3) social norms + psychoeducation, or (4) placebo control. The social norms condition was a
personalized normative feedback (PNF) intervention, which is tailored to the individual and has
been effective at reducing problematic behaviors such as binge drinking (e.g., Dotson et al.,
2015; LaBrie et al., 2013) and gambling (Neighbors et al., 2015). The psychoeducation condition
was modeled after traditional MHS-reduction interventions, and provided information about
depression and treatment (e.g., Dalky, 2012). The placebo control condition was also a PNF
intervention, but provided normative information about unrelated behaviors (e.g., texting rates).
Our outcomes of interest are primarily mental health stigma, help-seeking, depression,
and actual rates of mental health service utilization at 2-week and 3-month follow-up after the
intervention. Similar to Mahler and colleagues (2008), we predict a main effect of treatment,
such that the social norms + psychoeducation condition will lead to reduced mental health stigma
and greater help-seeking and treatment utilization than either social norms or psychoeducation
alone, and that all 3 intervention conditions will be more beneficial than the placebo control.
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Although there have been few explicit comparisons of social norms vs. educational interventions,
some research suggests that receiving information about actual peer behaviors may be more
beneficial than education alone in reducing stigma (Murrar et al., 2020). As a result, we also
predict that the social norms condition will be superior to psychoeducation in reducing MHS and
increasing help-seeking.
Furthermore, we predict that ethnicity and cultural orientation will moderate treatment
effects on MHS, help-seeking, and treatment utilization. Due to increased sensitivity to perceived
norms, and greater preference for conformity among Asian Americans, we predict that social
norms intervention effects will be greater for Asian than European Americans. However, treating
Asian Americans and European Americans as single, homogeneous groups can be problematic,
as there can be enormous within-group variation in cultural orientation (Green et al., 2005).
Therefore, we predict that interdependence will also moderate results, such that intervention
effects will be greater for those higher in interdependence compared to those lower in
interdependence.
Finally, we will test participants’ mental health stigma as a mechanism of change for
intervention effects on help-seeking and treatment utilization at follow-up. Due to the strong
negative relationship between MHS and help-seeking behaviors (Clement et al., 2015), we
predict that all 3 interventions (vs. placebo control) will decrease MHS, which will then lead to
increased help-seeking and treatment utilization. Thus, we predict that changes in MHS will
mediate intervention effects on help-seeking and utilization. Ethnicity and culture should also
moderate the mediating effects of MHS, such that the mediation effects will be stronger for
Asian Americans and those higher in interdependence (vs. European Americans and those lower
in interdependence), due to the cultural relevance of social norms interventions.
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Significance
To our knowledge, the present study is the first to apply a social norms approach to target
help-seeking for individuals with unmet mental health concerns. Furthermore, this is one of the
first mental health interventions to explore both ethnic and cultural differences in outcomes.
Although a prior study found that watching videos with actors normalizing depression reduced
MHS with Asian Americans (Botha et al., 2017), they did not include a comparison group, nor
was their sample clinical, so they did not measure help-seeking or treatment utilization as
outcomes. Results from the proposed study may help clarify cross-cultural effects of social
norms interventions and have implications for addressing disparities in treatment utilization and
increasing mental health use for Asian Americans in need.
Chapter 1: Method
Creating Peer Norms
To identify actual peer norms for the social norms condition, 1,152 University of
Southern California (USC) students in the psychology subject pool during the Spring 2020 and
Fall 2021 semesters completed a survey with 5 questions assessing their attitudes towards mental
illness and utilizing mental health services on a 7-point Likert scale ranging from 1 (i.e., strongly
disagree) to 7 (i.e., strongly agree). A sample item states, “I am supportive and accepting of my
peers who utilize psychological treatment for their mental health problems.” The 5 questions
were adapted from a previous study assessing peer norms regarding diversity (Murrar et al.,
2020). This data was aggregated and used to construct peer norms in the subsequent personalized
social norms intervention.
To obtain the norms for the placebo control condition, the same psychology subject pool
students were also asked about their frequency of texting, listening to music, and playing video
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games. Similarly, these responses were aggregated to create norms for the placebo control
condition.
The social reference group (i.e., other USC undergraduates) was chosen given research
demonstrating that the more general referent of same-campus students was better at improving
outcome than more specific reference groups (e.g., same-campus students of same gender, race,
or Greek affiliation; LaBrie et al., 2011).
Main Study
Eligibility. Our study recruited USC undergraduates with unmet mental health needs.
Specific eligibility requirements included: (1) age 18 years or older, (2) Asian or European
American ethnicity, (3) fluency in English, (4) a score of 10 or higher on the PHQ-9, and (5) no
current use of any psychotherapeutic services or psychiatric medication.
Procedure
Prescreen. Potential participants were recruited through the University of Southern
California (USC) psychology subject pool or through advertisements posted around campus and
via email listservs. For the former, eligibility was determined by the participants’ answers on a
prescreening survey that was sent to the entire psychology subject pool. For the latter, research
assistants posted flyers around busy areas across campus (e.g., cafeterias, dorm buildings, large
lecture halls) or requested that lecturers to distribute the flyers via email to the students in their
classes. The flyers included information about the study, along with a link and Quick Response
(QR) code to a brief survey on Qualtrics to determine eligibility (see Appendix A). If the
respondent was a USC student, they were then directed to follow-up questions assessing
perceptions of peer attitudes towards mental illness and treatment utilization. Lastly, upon
completion of the pre-screening surveys, eligible participants were invited to participate in an
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online study for either subject pool credit or monetary incentive (i.e., $10 for a 20-30 minute
study). Participants were recruited and enrolled in the study between January and December
2021.
Conditions. For the intervention, participants were randomly assigned to 1 of 4
conditions: (1) social norms only, (2) psychoeducation only, (3) social norms + psychoeducation,
or (4) placebo control. These four conditions are based on methods used in previous social norms
interventions for college students (Mahler et al., 2008; Hummer, 2019; LaBrie et al., 2013). In
the social norms condition, participants received a personalized slideshow presentation
comparing their perceptions of mental health norms, as reported during the eligibility screening,
to the actual beliefs of their peers. In the psychoeducation condition, participants watched a
slideshow providing psychoeducation on depression and its etiology, and information about the
benefits of mental health treatment. Participants received both PNF and psychoeducation
slideshows in the social norms + psychoeducation condition. Finally, in the placebo control
condition, participants received personalized normative information on topics unrelated to
mental health, such as frequency of texting, listening to music, and playing video games (LaBrie
et al., 2013).
Description of the Intervention. In all 4 conditions, participants were presented with a
slideshow presentation containing information in text and graphical format. Pre-recorded audio
guided the participant through the presentation, with relevant explanations of each portion of the
slides. The social norms intervention slides consisted of separate graphs presenting personalized
information comparing the participant’s self-endorsed attitudes with the normative sample. In the
psychoeducation condition, the slides consisted of detailed text and graphical explanations of
various components of depression, and reviews of effective treatments. In the social norms +
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psychoeducation condition, the slides provided both psychoeducation and personalized
normative feedback comparing the participant’s own attitudes with their peers. In the placebo
control condition, the slides consisted of graphs comparing the participant’s frequency of texting,
browsing social media, listening to music, and playing video games to the frequency of their
peers (See Appendix B).
Immediately following the intervention, questionnaires assessing mental health stigma,
treatment-seeking attitudes, and other relevant demographic variables were completed. Next,
participants were informed that they met a threshold indicating potential depression, as assessed
by their PHQ-9 scores during the prescreening survey, and they were offered an individual intake
session with a clinical psychology graduate student to assess the participant’s needs and direct
them to the appropriate resources. All participants were then given a list of potential mental
health referrals and relevant resources present on campus, regardless of whether they accepted or
rejected the intake session.
Follow-up
At 2-week and 3-month follow-up, participants were contacted to complete brief, 10-
minute follow-up surveys online for $5 each. The follow-up surveys assessed mental health
stigma, help-seeking, depression, and actual rates of mental health service utilization.
Participants were sent reminder emails 3 days apart, for a maximum of 5 reminders over 2
weeks.
Summary of Assessment Timepoints
Basic eligibility information was assessed during the prescreen (T1). Demographic
information, mental health stigma, and help-seeking attitudes, intention, and behaviors were
assessed immediately following the intervention (T2). Mental health stigma, help-seeking
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attitudes and intention, and service utilization were assessed 2 weeks later (T3), and 3 months
following the intervention (T4). Depression was measured during the prescreen (T1) and at 2-
week and 3-month follow up (T3 and T4).
Measures
Demographic and cultural information. Relevant demographic information such as
age, sexuality, gender, ethnicity, and income, and other cultural variables (e.g., interdependence)
were assessed at T2. See Table 1 for detailed information.
Perceived norms. A 6-item measure of perceived norms was created by tailoring
questions assessing perceived norms towards diversity developed by Murrar and colleagues
(2020). The adapted measure assessed what percent of the participant’s peers would approve of
the use of mental health services on a scale from 0-100%. A sample item states, “What
percentage of USC students would be supportive and accepting of peers who utilize
psychological treatment for their mental health problems?” Perceived norms were assessed
during the prescreen at T1. Responses from 1,152 respondents were aggregated to create these
peer norms. There was a significant difference between the actual norms that exist among the
USC student body, and participants’ perceptions of their peer norms, such that perceptions of
peer mental health stigma were much higher than their actual attitudes. See Table 2 for more
information.
Mental health stigma. The Social Distance Scale (SDS; Bogardus, 1933) assesses
participants’ hypothetical willingness to engage with a person with a mental illness in a series of
interactions (e.g., “how would you feel about working with a person with a mental illness?”) on a
4-point Likert scale ranging from 1 (i.e., “definitely unwilling”) to 4 (i.e., “definitely willing”).
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17
The SDS has good validity and reliability in other work (Penn et al., 1994), and was used to
measure personal stigma.
Societal stigma was measured by adapting the SDS to assess the social distancing desires
others might have towards a person with mental illness (e.g., “how would most people feel about
working with a person with a mental illness?”. This adapted version of the SDS had good
reliability in previous studies (α = 0.85; Wang & Huey, 2022).
Mental health stigma was assessed at T2, T3, and T4. In the current sample, reliability
was excellent for both personal (α = .90-.93) and societal stigma (α = .91-.92), across the 3 time
periods.
Help-seeking attitudes. The Inventory of Attitudes Towards Seeking Mental Health
Services (IASMHS; Mackenzie et al., 2004) assesses participants’ attitudes towards seeking help
from a mental health professional on a 5-point scale ranging from 0 (disagree) to 4 (agree). The
scale has 3 dimensions: psychological openness, help-seeking propensity, and indifference to
stigma. A sample question states, “I would feel uneasy going to a professional because of what
some people would think.” The IASMHS has good validity and reliability (Mackenzie et al.,
2004; Perry et al., 2014). Help-seeking attitudes were measured at T2, T3, and T4. Reliability for
the IASMHS was good across the three time periods (α = .84-.88).
Help-seeking intentions. The General Help Seeking Questionnaire (GHSQ; Wilson et
al., 2005) measures participants’ intentions to seek help from sources ranging from informal to
formal sources on a 7-point scale from 1 (i.e., extremely unlikely) to 7 (i.e., extremely likely).
Sources of help that the participant can endorse include “intimate partner,” “mental health
professional” and “minister or religious leader.” This measure has good consistency and
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18
excellent reliability in former samples (Wilson et al., 2005), and was assessed at T2, T3, and T4.
Reliability was in the marginal to acceptable range (α = .65-.70) for the current sample.
Help-seeking behaviors. Help-seeking behaviors were measured by the acceptance or
rejection of an individual intake session offered to each participant upon completion of the
intervention. If the intake session was accepted, the participant was directed to a separate website
to schedule a 30-minute appointment with a clinical psychology graduate-level therapist. During
the intake, the therapist obtained information on the participant’s presenting problems, provided
brief psychoeducation about depression, then presented the participant with appropriate referrals
based on their insurance, and resources for free mental health services available at the USC
campus. If the intake session was rejected, the participant was directly sent to a webpage with a
list of mental health resources available on campus, with relevant contact information. Previous
studies show that behavioral observations of acceptance or rejection of related study items can be
important for the measurement of the construct of interest (Barry & Tyler, 2009; Focella et al.,
2016; Lefcourt, 2013; Stone et al., 1994). Help-seeking behavior was only measured at T2.
Utilization of services. To be eligible for the study, participants could not have current
use of mental health services. In the prescreen, two yes/no questions were asked to assess for
current utilization: (1) “Are you currently receiving any mental health counseling?” and (2) “Are
you currently taking any medication for mental health concerns?” Participants who answered
“yes” to either question on the prescreen were deemed ineligible for the study.
To assess utilization of mental health services 2-weeks and 3-months following the
intervention session, participants completed part 1 of the Treatment Inventory of Costs in
Patients with Psychiatric Disorders (TiC-P; Boumans et al., 2013) at 2-week and 3-month
follow-up (T3 and T4). These time periods were chosen to allow the participants adequate time
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19
to schedule appointments or make other arrangements to obtain help. The first part of the TiC-P
presents the participants with 14 yes/no questions on their utilization of relevant mental health
resources, such as a “general practitioner” or a “psychiatrist, psychologist, or psychotherapist in
private practice”. Reliability estimates of the TiC-P range from good to excellent (Boumans et
al., 2013). For the current study, the TiC-P was tailored to include mental health services more
widely available during the COVID-19 pandemic (e.g., therapy app). Reliability was acceptable
for the current sample (α = .76).
Depression. The Patient Health Questionnaire-9 (PHQ-9; Kroenke & Spitzer, 2002) is a
9-item inventory assessing for symptoms of clinical depression, such as “little interest or
pleasure in doing things,” and “feeling down, depressed or hopeless,” as defined by the DSM-5,
with scores of 10 or greater indicating that client may meet threshold for clinical levels of
depression (Kroenke et al., 2009). The PHQ-9 has good reliability and validity across diverse
ethnic and cultural groups (e.g., Becker et al., 2009; Huang et al., 2006).
Depression was assessed at T1, to screen for eligible participants, then at 2-week and 3-
month follow-up. Depression was not assessed immediately following the intervention (i.e., T2)
due to the theorized mechanism behind changes in depression; we predicted the intervention
would lead to decreased MHS, which would then facilitate help-seeking, and would in turn
improve mental health symptoms. Changes in depression levels are therefore unlikely to occur
immediately after the intervention, as participants would need to take time to access mental
health services. Reliability for the PHQ-9 was in the acceptable to good range (α = .75-.86).
Cultural Values. The Singelis Self-Construal Scale (Singelis, 1994) measures the extent
to which participants adhere to interdependent or independent cultural values. The
interdependent and independent subscales each consist of 15 questions (e.g., “I enjoy being
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unique and different from others in many respects”) rated on a 7-point Likert scale ranging from
1 (i.e., “strongly agree”) to 7 (i.e., “strongly disagree”). Cronbach’s alpha for the subscales range
from the high .60’s to the mid-.70’s (Singelis, 1994). The items on the two subscales were
totaled, and reliability was acceptable for the independent subscale (α = .71) and good for the
interdependent subscale (α = .81). The Singelis Self-Construal Scale was administered
immediately following the intervention (T2). Given research linking interdependence with
stigma, only this subscale was analyzed as a potential moderator of intervention effects.
Manipulation Check. Following the intervention, participants were asked, “What were
the videos you watched about?” with the choices being (1) How my views about mental illness
compare to my peers (i.e., social norms condition), (2) Facts and information about depression
(i.e., psychoeducation condition), (3) Facts and information about depression AND how my
views about mental illness compare to my peers (i.e., social norms + psychoeducation condition),
or (4) How my texting, social media use, music use, and gaming behaviors compare to my peers
(i.e., placebo control condition). Participants who failed the manipulation check were removed
from the final analyses.
Data Analysis
Primary analyses. The outcomes of interest in the following multilevel linear models
(MLM) included mental health stigma, help-seeking attitudes, intentions, and behaviors,
utilization of mental health services, and depression, with gender, sexual orientation, and T1
depression controlled for as covariates. The R (R Core Team, 2016) software packages titled
“lme4” (Bates et al., 2013) and “glmmTMB” were used for MLM analyses with continuous
variables and binary variables, respectively. First, empty models with no predictors were
conducted to assess for intraclass correlation coefficients, which determined the suitability of
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21
MLM for the data structure. Second, time was entered as a predictor to assess for changes in
mental health outcomes over time, with assessment periods (i.e., Times 1-4) nested within
individuals. Third, condition was added as a predictor to assess intervention effects over time.
Fourth, the interaction between condition and ethnicity was entered into the models to test for the
moderating effects of race. Lastly, the interaction between condition and interdependence were
tested to assess for moderator effects. All predictor variables were level 2 between-subjects
variables (e.g., condition, ethnicity, interdependence).
Help-seeking behavior (i.e., acceptance or rejection of a mental health intake) was only
measured at T2. As a result, a multinomial logistic regression was conducted to assess condition
effects on help-seeking behavior at that discrete time point.
Mediation analyses. Mental health stigma was tested as a mediator of the relationships
between condition and help-seeking/utilization outcomes. Specifically, both personal and
societal forms of mental health stigma at T2 were tested as mechanisms of condition effects on
help-seeking and treatment utilization at T3 and T4. Additionally, ethnicity and interdependence
were tested as moderators of mediation effects. All mediation analyses were conducted using the
PROCESS macro (Hayes, 2022) for SPSS.

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Chapter 2: Results
Participant Characteristics
One thousand, one hundred and fifty-two USC subject pool students completed the
prescreening survey between January to October 2021. Of the 1,152 completers, 161 were
eligible and then invited to participate in the study for subject pool credit. An additional 370
students were recruited through flyers distributed throughout campus or via email listservs, and
51 were eligible and invited to participate for monetary incentive. Of the 212 total who were
eligible, 75 students signed up for the study and had their personalized slideshows created. Nine
students failed to complete the online intervention, and 5 students failed the manipulation check,
for a final sample of 61 (Figure 1). Although participants were randomly assigned to condition,
there were less than half as many participants in the social norms only condition than in the other
three (i.e., 8 vs. 17-19 participants), due to chance during the randomization process. Follow-up
ANOVAs found no significant difference between conditions in drop-out rates. As a result, the
social norms only condition was combined with the social norms + psychoeducation condition to
create a combined social norms condition. Results with the 4 conditions run separately can be
found in Appendix C.
Participants’ mean age was 19.0 (SD = 1.2). They were predominantly female (68.9%)
and Asian (60.7%). Of the Asian/Asian American participants, 43.2% identified as
Chinese/Chinese American, 16.2% as Korean/Korean American, 8.1% as
Vietnamese/Vietnamese American, and 32.4% as Other (e.g., Chinese Indonesian, Taiwanese
American, half-Korean half-Japanese, etc.). Most participants were born in the United States
(77%), heterosexual (65.6%), and from households with an income of over $100,000 (88.5%).
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23
On average, participants endorsed high levels of interdependence (M = 72.48, SD = 11.88). See
Table 1.
Assessment of Necessity of MLM
Intraclass correlations (ICCs), which measure the amount of variance explained by
clustering, were calculated to determine whether MLM was appropriate for modeling the
changes in outcomes over time. Empty models were conducted for each of the mental health
outcomes, with time points clustered within individuals. The ICCs ranged from .32 to .89,
suggesting a high levels of clustering. In addition, the design effects (deff), which account for
variance explained by the study design (i.e., clustered time points within individuals), ranged
from 1.64-2.78. According to Lai and Kwok (2015), multilevel modeling is recommended when
deff > 1.1; as a result, multilevel models were conducted to assess for outcome changes over
time.
Changes in Mental Health Outcomes over Time
Across all participants, trajectories of depression significantly decreased over time,
estimate = .17, SE = .04, p < .01, and rates of mental health utilization significantly increased
over time, estimate = .27, SE = .04, p < .01. There were no significant changes in help-seeking
attitudes or intentions, or in personal or societal mental health stigma, over time, p > .05 (Figure
2). See Table 3 for more detailed descriptive information.
Intervention Effects
MLM was used to assess the main effects of condition on the continuous mental health
outcomes (i.e., personal and societal MHS, help-seeking attitudes and intentions, and depression)
over time. The specific type of model used was a linear growth model, with gender, sexual
orientation, and T1 depression controlled for as covariates. Gender and sexual orientation were
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24
controlled for given research showing that women and members of the LGBTQ+ community
seek services at much higher rates than men or those who do not identify as LGBTQ+ (e.g.,
Gonzalez et al., 2002; Platt et al., 2018). There were no condition effects on any of the mental
health outcomes, including personal mental health stigma, estimate = -.03, SE = .19, p = .89,
societal mental health stigma, estimate = .08, SE = .33, p = .82, help-seeking attitudes, estimate =
.17, SE = .57, p = .76, help-seeking intentions, estimate = -.61, SE = .85, p = .47, or depression,
estimate = -.24, SE = .44, p = .58.
Multilevel logistic regression was conducted to assess for condition effects on mental
health utilization at the follow-up periods, using the same covariates. No condition effects were
found, estimate = .17, SE = .25, p = .51, indicating no differences in utilization trajectories by
condition.
Finally, multinomial logistic regression was to assess for condition effects on help-
seeking behavior at T2 only. There was no significant difference between conditions in
likelihood of accepting the intake post-intervention, B = -.41 to -.32, SE = .51 to .74, p > .05.
In summary, we found no significant intervention effects on any of the mental health
outcomes. See Table 4 for detailed information.
Ethnicity and Interdependence as Potential Moderators
To assess whether ethnicity moderated treatment effects, the interaction between
condition and ethnicity (i.e., Asian or European American) was added to the multilevel models,
with gender, sexuality, and T1 depression included as covariates, across the three time periods.
There was no significant interaction between condition and ethnicity on personal mental health
stigma, estimate = -.22, SE = .51, p = .66, societal mental health stigma, estimate = .59, SE =
.88, p = .51, help-seeking attitudes, estimate = -1.01, SE = 2.22, p = .65, help-seeking intentions,
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25
estimate = -1.23, SE = 1.53, p = .42, depression, estimate = -1.85, SE = 1.10, p = .10, or mental
health utilization, estimate = .20, SE = 1.14, p = .86. Similarly, there was no interaction between
condition and ethnicity on T2 help-seeking behaviors, χ²(4, N = 51) = 3.14, p = .80. See Table 5.
Next, to assess interdependence as a possible moderator, the effects of the interaction
between condition and interdependence on mental health outcome trajectories were assessed.
Interdependence significantly moderated condition effects on mental health service utilization
over time, estimate = -.05, SE = .02, p =.04, such that the efficacy of the combined social norms
condition (vs. psychoeducation and placebo control) increased with greater levels of reported
interdependence. Participants were divided into 3 categories for post-hoc analyses: low
interdependence, medium interdependence, and high interdependence. The combined condition
was more effective than psychoeducation and placebo at increasing rates of utilization for those
with high interdependence, but there were no condition effects for those with medium or low
levels of interdependence (Figure 3). Similarly, interdependence significantly moderated
condition effects on personal mental health stigma over time, estimate = -.03, SE = .02, p = .04,
such that the combined social norms condition led to lower levels of stigma than the
psychoeducation or placebo condition for those high in interdependence. There were no
differences between condition for those with low or medium levels of interdependence (Figure
4).
There was no significant interaction between condition and interdependence for societal
mental health stigma, estimate = .02, SE = .03, p = .52, help-seeking attitudes, estimate = -.04,
SE = .07, p = .64, help-seeking intentions, estimate = .06, SE = .05, p = .20, and depression,
estimate = -.01, SE = .03, p = .72, trajectories.
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To summarize, interdependence significantly moderated condition effects on trajectories
of mental health utilization and personal mental health stigma over time, with the combined
condition being most effective for those high in interdependence. Ethnicity did not significantly
moderate intervention effects.
Mental Health Stigma as Potential Mediator
A series of mediation analyses were conducted to assess for the mediating role of T2
mental health stigma on the relationship between intervention condition and outcome at T3 and
T4. As recommended by Preacher and Hayes (2008), a bootstrapped mediation analysis with
5000 iterations of random sampling with replacement was performed. First, paths a (i.e., effect of
IV on mediator), b (i.e., effect of mediator on outcome), and c (i.e., direct effect of IV on DV)
were calculated. Then, path ab (i.e., the indirect effect of the independent variable on the
dependent variable) was calculated to assess for successful mediation (Hayes & Rockwood,
2017; Zhao et al., 2010). Both personal and societal mental health stigma at T2 were assessed as
mediators.
Personal mental health stigma did not mediate effects of condition on help-seeking
attitudes at T3, a*b = -.06, SE = 1.40, CI95% = [-3.60, 1.82] or T4, a*b = -.06, SE = 1.40, CI95% =
[-352, 1.77]. Personal MHS also did not significantly mediate effect of condition on help-seeking
intentions (T3: a*b = -.04, SE = .28, CI95% = [-.79, .38]; T4: a*b = .36, SE = .30, CI95% = [-3.22,
1.95]), or depression (T3: a*b = .04, SE = .15, CI95% = [-.39, .33]; T4: a*b = .02, SE = .54, CI95%
= [-1.07, .90]). See Table 7. Similarly, societal mental health stigma did not mediate condition
effects on help-seeking attitudes (T3: a*b = .16, SE = 1.10, CI95% = [-.74, 1.02]; T4: a*b = .58,
SE = 1.29, CI95% = [-.85, 2.79]), help-seeking intentions (T3: a*b = -.15, SE = .40, CI95% = [-
SOCIAL NORMS INTERVENTION TO PROMOTE HELP-SEEKING

27
1.03, .32]; T4: a*b = -.02, SE = .30, CI95% = [-.84, .62]), or depression (T3: a*b = -.19, SE = .33,
CI95% = [-.87, .25]; T4: a*b = -.37, SE = .57, CI95% = [-1.30, .52]). See Table 8.
Moderated mediation analyses were then conducted to assess whether the mediating role
of mental health stigma differed by ethnicity or interdependence. For ethnicity, which was a
dichotomous variable (i.e., Asian or European American), the difference between conditional
indirect effects was calculated as an index of moderated mediation. For interdependence, the
difference between conditional effects taken at the 16
th
, 50
th
, and 84
th
percentiles were calculated
as the index of moderated mediation. With personal mental health stigma as a mediator,
mediation effects did not differ by ethnicity for help-seeking attitudes (T3: index of moderated
mediation= -2.96, SE = 1.12, CI95% = [-10.74, 1.26]; T4: index = -1.69, SE = 3.67, CI95% = [-
8.31, 3.27]), help-seeking intentions (T3: index = .47, SE = 1.12, CI95% = -1.39, 3.64]; T4: index
= .05, SE = 3.67, CI95% = [-1.46, 3.07]), or depression (T3: index = -.82, SE = .60, CI95% = [-.50,
2.89]; T4: index = .99, SE = 1.30, CI95% = [-1.73, 5.79]; see Table 9). There were also no ethnic
difference in societal mental health stigma mediation effects for help-seeking attitudes (T3: index
= -2.35, SE = 4.12, CI95% = [-6.24, 8.13]; T4: index = -2.30, SE = 4.48, CI95% = [-13.41, 4.65]),
help-seeking intentions (T3: index = .34, SE = 1.25, CI95% = [-1.75, 3.32]; T4: index = -.09, SE =
3.67, CI95% = [-3.77, 3.66]), or depression (T3: index = 1.06, SE = 1.07, CI95% = [-.54, 2.75]; T4:
index = 1.23, SE = 2.72, CI95% = [-2.78, 8.09]). See Table 10.
Similarly, interdependence did not moderate the mediating effects of personal mental
health stigma on help-seeking attitudes (T3: index = .01, SE = .16, CI95% = [-.24, .21]; T4: index
= -.05, SE = .11, CI95% = [-.48, .19]), help-seeking intentions (T3: index = .003, SE = .03, CI95% =
[-.06, .06]; T4: index = -.001, SE = .03, CI95% = [-.07, .04]), and depression (T3: index = .01, SE
= .02, CI95% = [-.04, .07]; T4: index = -.01, SE = .06, CI95% = [-.12, .10]). See Table 11. Nor did
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28
interdependence moderate the mediating effects of societal MHS on help-seeking attitudes (T3:
index = -.13, SE = .08, CI95% = [-.39, .05]; T4: index = -.07, SE = .14, CI95% = [-.26, .08]),
intentions (T3: index = .02, SE = .02, CI95% = [-.05, .09]; T4: index = .0004, SE = .03, CI95% = [-
.08, .07]), or depression (T3: index = .03, SE = .02, CI95% = [-.02, .08]; T4: index = .06, SE = .06,
CI95% = [-.06, .16]). See Table 12.
In conclusion, personal or societal mental health stigma did not significantly mediate
condition effects on any of the mental health outcomes. Ethnicity and interdependence also did
not significantly moderate the mediating effects of stigma.
Chapter 3: Discussion
In the midst of the deadliest pandemic in the past century, it is unsurprising that mental
health problems have skyrocketed across the United States. This is especially concerning for
Asian American college students, who are not only in a vulnerable stage of their development,
but have also been disproportionately affected by the pandemic due to surging rates of anti-Asian
bias and discrimination (Hahm et al., 2021, Wu et al., 2020). Despite the clear need for mental
health services at this critical time, rates of utilization are low across college campuses, and
lowest among Asian American college students (Zhou, 2020). To target disparities with this at-
risk population, the present study piloted a combined social norms intervention for help-seeking,
in comparison to standard intervention alone (i.e., psychoeducation) and placebo control. To our
knowledge this is the first study to apply a social norms paradigm to help-seeking for individuals
with unmet mental health needs. This is also one of the few studies to test for differential effects
in a mental health intervention by ethnicity and cultural values.
Although there was no significant main effect of condition, interdependence significantly
moderated condition effects on the trajectory of mental health utilization, such that the benefits
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29
of the combined social norms intervention (vs. psychoeducation or placebo control) on
utilization were greatest for those who were high in interdependent cultural values. There were
no significant condition effects on utilization rates for those with medium or low levels of
interdependence. Similarly, interdependence moderated condition effects on trajectories of
personal mental health stigma, such that the social norms condition (vs. psychoeducation or
placebo control) led to decreased stigma for only those high in interdependence.
Our findings suggest that adherence to cultural values may be more important than
ethnicity when considering the suitability of a social norms intervention. Contrary to findings in
previous studies (e.g., Wang & Huey, 2022) our Asian participants endorsed similar rates of
interdependence as European participants, and surprisingly higher rates of help-seeking and
service utilization than European American participants. However, other studies have found that
other demographic variables besides ethnicity (e.g., age, political affiliation) are associated with
higher MHS (DeLuca & Yanos, 2015; Schomerus et al., 2015). This illustrates the potential
importance of individualizing treatment to the client’s specific values and refraining from
treating ethnic groups as homogeneous.
We also found increases in mental health utilization rates and decreases in depressive
symptoms over time, independent of condition. At the time of the intervention, none of our
participants were engaged in services, but this surged to a staggering 51% at 3-month follow-up.
In contrast, national estimates find that only 20% of college students accessed mental health
services during the first year of COVID-19 pandemic (Zhou, 2020), which overlapped with the
timing of the present study, suggesting that our participants’ rates of utilization were
significantly higher than national norms. Furthermore, their depression scores decreased
significantly over time. At prescreen, participants’ average depression scores were consistent
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30
with moderately severe depression (PHQ9 = 15.4), but their scores dropped to the moderate
range (PHQ9 = 11.3) at 2-week follow-up, and then finally to subclinical threshold (PHQ9 = 9.1)
at 3-month follow-up.
It is unclear what the mechanism of change was that drove these improvements, but given
that these remarkable changes occurred in such a short period of time, our treatment may have
contributed in some way. One possibility is that by providing information on services offered to
students on campus, and offering an individual meeting with a trained therapist, the access
barrier to help-seeking was removed. Indeed, an informal assessment of the participants’
knowledge of USC health services during the intake sessions found that most participants were
unaware of the free mental health services available on campus. Simply providing this
information may have unintentionally served as an intervention for help-seeking on its own,
which may have contributed to subsequent decreases in depression. However, without a no-
treatment control condition for comparison, it is impossible to determine whether the high rates
of utilization and decreased depression were due to participation in our study, or whether these
changes would have occurred regardless, due to the episodic course of depression or regression
to the mean.
To summarize, this pilot study presents some preliminary evidence for the effectiveness
of social norms intervention, either alone or when combined with a traditional psychoeducational
treatment, for targeting mental health disparities among college students. Although there was no
main effect of condition, the combined social norms condition was more effective than the
traditional intervention alone or placebo at increasing service utilization and decreasing mental
health stigma for students who adhered to an interdependent cultural construal. Furthermore,
participants’ rates of service utilization, regardless of treatment condition, skyrocketed from 0%
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31
at prescreen to 51.4% at 3-month follow-up, and depression scores dropped from the moderately
severe to the subclinical range. This suggests some benefits to participating in our study, for
reasons that should be investigated further in future studies.
Despite these promising findings, this study has several limitations that need to be
addressed. First, the small sample size made interpretation of study results challenging. A power
analysis was conducted with the program G*Power 3 (Faul et al., 2007) for a fixed effects
ANOVA with 8 groups and 7 degrees of freedom. The Type I error rate was set to 0.05,
reflecting a two-tailed test, and the power (1-Type II error rate) was set to 0.8. In order to detect
a small effect size (f = 0.1), a sample size of 1,443 would be needed; to detect a medium effect
size (f = 0.25), a sample size of 237 would be needed; to detect a large effect size (f = 0.4), a
sample size of 97 would be needed. Due to difficulty with recruitment of this very specific
clinical population, our sample size was only 61. Although our MLM statistical approach was
more powerful than traditional linear methods, our sample still lacked the necessary power to
detect more subtle effects.
Second, because of the small sample size, we had to combine the two social norms
conditions (i.e., social norms + psychoeducation and social norms only). Thus, it was difficult to
determine whether the significant effects of the combined condition can be attributed to the
social norms aspect alone, or rather to the synergistic effects of social norms and
psychoeducation. However, it seems unlikely that effects of the combined condition are due to
psychoeducation alone, given the poorer outcomes of individuals in the psychoeducation
condition.
Third, the differences between perceived norms and actual norms in our study were
modest compared to those in other PNFs. For example, Hummer (2019) found that student
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32
estimates of the average number of drinks their peers consumed weekly was nearly double the
actual rates (e.g., 11.72 vs. 6.53 drinks). In contrast, the discrepancies between our perceived and
actual norms were not as prominent. For example, with the item, “Are you supportive and
accepting of your peers who utilize psychological treatment for mental health problems?,”
participants on average perceived that 77% of their peers agreed with that statement, while the
actual percentage was 97%. The mere 20% difference was not as dramatic as it has been in
previous studies; thus awareness of this discrepancy may not have been as impactful and
effective for eliciting change as with other PNF interventions.
The present study piloted a social norms approach to help-seeking with depressed Asian
and European American college students at the University of Southern California. Our results
revealed some preliminary evidence for the potential benefits of social norms intervention (alone
and combined with psychoeducation) for help-seeking and mental health service utilization, but
due to the small sample size, our interpretations are limited. Recommendations for future
research are discussed below.
For the current study, we utilized personalized normative feedback (PNF) as our social
norms intervention of choice, given the considerable evidence demonstrating its benefits for
college students (e.g., Hummer, 2019; LaBrie et al., 2011). However, most of these studies target
specific problematic behaviors that affect only a small portion of the population (e.g., binge
drinking, gambling). PNF interventions that target more widespread problems typically include
metrics that are easier to measure (e.g., water consumption). Mental health utilization, which
may have been a need for only a fraction of the population years ago, is now a widespread need,
especially across college campuses during the COVID-19 pandemic (e.g., Zhou, 2020; The
Healthy Minds Study, 2020). As a result, PNF may not be the most efficient method to
SOCIAL NORMS INTERVENTION TO PROMOTE HELP-SEEKING

33
encourage treatment engagement with a vast student body, and future studies should test other
approaches to social norming for help-seeking.
Murrar and colleagues (2020) recently tested the effects of several different social norms
interventions to increase pro-diversity attitudes across the University of Wisconsin-Madison. The
methods they piloted included watching a short video of peers discussing their normative views
regarding diversity, viewing a poster with statistics of pro-diversity and inclusion attitudes across
campus, or even reading an email from the Dean that disseminated those statistics. All of these
time-efficient methods were found to be effective for decreasing bias and increasing pro-
diversity attitudes and behaviors among UW-Madison students. Thus, future interventions
targeting help-seeking among students may benefit from employing one of these more easily
accessible strategies. In our study, we collected responses from over a thousand USC students
that can be easily distributed without personalization across campus via similar emails or posters.
Future studies should also investigate the efficacy of depersonalized vs. personalized social
norms interventions, to weigh the cost-benefits of mass distribution of normative information.
Finally, our sample included a fairly niche sample of Asian and European American
college students at the University of Southern California. In general, they were mostly female,
psychology majors, high SES, and more likely to identify as LGBQT+ than the general
American public (e.g., 34.4% vs. 7.1%; Jones, 2022). As a result, they may not have had the
same access or stigma barriers as other groups in the United States, hence their extremely high
levels of treatment utilization at follow-up. It is unclear whether this intervention would be
beneficial for other groups. More research is needed to examine whether effects can generalize to
other diverse groups and presenting problems. Furthermore, future research should include a
SOCIAL NORMS INTERVENTION TO PROMOTE HELP-SEEKING

34
much larger sample size, and perhaps utilize stratified random sampling to ensure equal group
sizes.
In conclusion, the current pilot study presents insights on the benefits of applying a social
norms approach for help-seeking interventions. Our results highlighted the potential cultural
relevancy of social norms interventions, and found that any form of social norms intervention
was superior to psychoeducation alone or placebo control for increasing rates of mental health
utilization and decreasing stigma over time for those with an interdependence cultural construal.
This intervention may be especially important for Asian American young adults who are
simultaneously affected by the “mental health crisis” across college campuses, and the negative
effects of COVID-19 related anti-Asian racism. Our pilot study demonstrated the feasibility of a
social norms intervention for a large study body, and has implications for reducing stigma-
related barriers to mental health utilization for those with unmet mental health needs.
SOCIAL NORMS INTERVENTION TO PROMOTE HELP-SEEKING

35
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Tables
Table 1
Participant Characteristics
Variable
Age, M (SD) 19.0 (1.2)
Gender,

N (%)
Female 42 (68.9%)
Male
Non-binary
18 (29.5%)
1 (1.6%)

Sexual Orientation, N (%)
Heterosexual 40 (65.6%)
Gay or Lesbian 4 (6.6%)
Bisexual 16 (26.2%)
Other 1 (1.6%)
Race/Ethnicity,

N (%)
White 24 (39.3%)
Asian 37 (60.7%)
Chinese/Chinese American 16 (26.2%)
Korean/Korean American 6 (9.8%)
Vietnamese/Vietnamese American 3 (4.9%)
Other Asian (e.g., Chinese Indonesian, Taiwanese American, etc.) 12 (19.7%)
Place of Birth,

N (%)
USA 47 (77.0%)
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49
Europe 4 (6.6%)
Asia 10 (16.4%)
Cultural Construal, M (SD)
Interdependence 72.48 (11.88)
Independence 69.75 (10.37)
Interdependence – Independence (Int-Ind) 2.72 (14.9)
Household Income, N (%)
$0-50,000 7 (11.5%)
$50,001-75,000 13 (21.3%)
$75,001-100,000 6 (9.8%)
$100,000-150,000 8 (13.1%)
$150,000-250,000 8 (13.1%)
Greater than $250,000 19 (31.1%)

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50
Table 2
Aggregate peer norms vs. perceptions of peer norms (N = 1,152)
Item Actual Peer
Norms
Perception of
Peer Norms

Do you do your best to behave inclusively
towards those with mental illness?
98% 72.8%
Do you think it is shameful to see a therapist for
mental health problems?
4% 26.4%
Would you react negatively towards someone
who said or did something discriminatory
against those with mental illness?
90% 72.5%
Are you welcoming and accommodating towards
those with mental illness?
94%

72.2%
Are you supportive and accepting of your peers
who utilize psychological treatment for mental
health problems?
97% 77%
Do you think seeking psychological treatment
for mental health problems is a good thing?
99.6% 80.4%
Note. Results are percentages of participants who answered “yes” for the questions.

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Table 3
Mental Health Descriptive Information
Variables
Combined Social
Norms
Psychoeducation
T1 N=18
T2 N= 14
T3 N=9
Control
T1 N =19
T2 N=13
T3 N=10
T1 N=24
T2 N=21
T3 N=18

Personal mental health stigma (SDS)
Time 2 13.1 (3.3) 12.6 (5.5) 13.8 (4.7)
Time 3 14.0 (3.2) 12.9 (5.7) 13.1 (3.5)
Time 4 13.8 (3.9) 11.8 (4.6) 14.1 (5.5)
Societal mental health stigma (SDS_S)
Time 2 17.8 (5.1) 19.4 (3.3) 18.5 (2.8)
Time 3 18.3 (5.2) 19.6 (1.7) 18.9 (4.2)
Time 4 18.3 (5.1) 18.6 (5.0) 20.1 (4.9)
Help-seeking attitudes (IASMHS)
Time 2 61.3 (13.3)

61.8 (12.8) 67.0 (14.3)
Time 3 64.6 (14.0) 64.8 (17.6) 66.8 (15.8)
Time 4 59.8 (13.6) 65.7 (20.1) 60.8 (13.6)
Help-seeking intentions (GHSQ)
Time 2 38.7 (7.4) 41.0 (7.5) 39.2 (9.5)
Time 3 39.3 (7.5) 41.4 (8.3) 41.3 (9.4)
Time 4 38.5 (7.3) 42.2 (10.4) 39.6 (7.5)
Help-seeking behaviors, N (%)
Time 2 9 (45.0%) 6 (46.2%) 6 (35.3%)
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Variables
Combined Social
Norms
Psychoeducation
T1 N=18
T2 N= 14
T3 N=9
Control
T1 N =19
T2 N=13
T3 N=10
T1 N=24
T2 N=21
T3 N=18

MH utilization, N (%)
Time 3 10 (47.6%) 7 (50.0%) 7 (53.8%)
Time 4 9 (50.0%) 5 (55.6%) 6 (60.0%)
Depressive symptoms (PHQ-9)

Time 1 14.8 (3.8) 15.2 (4.9) 16.5 (5.0)
Time 3 10.2 (4.6) 12.9 (4.5) 11.5 (4.4)
Time 4 8.1 (4.6) 13.0 (8.2) 7.6 (3.9)
Note. Results are means and standard deviations unless stated otherwise.

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Table 4
Models of Condition Effects on Mental Health Outcome Trajectories.

Outcome Estimate Std. Error df T value P value
Personal mental health stigma -.03 .19 87 -.01 .89
Societal mental health stigma .08 .33 92 .23 .82
Help-seeking attitudes (GHSQ) .17 .57 85 .30 .76
Help-seeking intentions (IASMHS) -.61 .85 90 -.72 .47
Depressive symptoms (PHQ-9) -.24 .44 93 -.56 .58
MH Utilization Rates .17 .25 N/A Z = .66 .51

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Moderation Analyses
Table 5
Condition x Ethnicity Interaction Models
Outcome Estimate Std. Error df T value P value
Personal mental health stigma -.22 .51 84 -.44 .66
Societal mental health stigma .59 .88 91 .67 .51
Help-seeking attitudes (IASMHS) -1.01 2.22 87 -.46 .65
Help-seeking intentions (GHSQ) -1.23 1.53 91 -.81 .42
Depressive symptoms (PHQ-9) -1.85 1.10 92 -1.67 .10
MH Utilization Rates .20 1.14 N/A Z = .18 .86

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Table 6
Condition x Interdependence Interaction Models
Outcome Estimate Std. Error df T value P value
Personal mental health stigma -.03 .02 86 -2.06 .04*
Societal mental health stigma .02 .03 91 .65 .52
Help-seeking attitudes (IASMHS) -.04 .07 88 -.49 .63
Help-seeking intentions (GHSQ) .06 .05 82 1.31 .20
Depressive symptoms (PHQ-9) -.01 .03 93 -.36 .72
MH Utilization Rates -.05 .02 N/A Z = -1.5 .04
Note. * denotes a significance value of p < .05.
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Mediation Analyses
Table 7
Personal mental health stigma mediation models
Outcome variable Effect of IV on
mediator (a)
Effect of mediator
on DV (b)
Effect of IV on
DV (c)
Indirect effect
(ab)
Help-seeking
attitudes (IASMHS)

Time 3 -.05 [-1.73, 1.63] 1.75 [.86, 2.64]* .94 [-4.91, 6.76] -.06 [-3.60, 1.82]
Time 4 .18 [-1.73, 2.07] 1.16 [-.03, 2.35] .94 [-4.91, 6.79] -.06 [-3.52, 1.77]
Help-seeking
intentions (GHSQ)

Time 3 -.27 [-1.91, 1.39] .16 [-.38, .70] 1.12 [-1.79, 4.02] -.04 [-.79, .38]
Time 4 -.22 [-1.81, 1.37] .08 [-.57, .74] 1.38 [-3.24, 5.99] -.36 [-3.22, 1.95]
Depressive symptoms
(PHQ-9)

Time 3 -.22 [-1.81, 1.37] -.18 [-4.6, .10] .75 [-.79, 2.30] .04 [-.39, .33]
Time 4 -.05 [-1.73, 1.63] -.51 [-1.01, -.01]* .13 [-2.11, 2.38] .02 [-1.07, .90]
Note. * denotes a significance value of p < .05
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Table 8
Societal mental health stigma mediation models
Outcome variable Effect of IV on
mediator (a)
Effect of
mediator on DV
(b)
Effect of IV on
DV (c)
Indirect effect
(ab)
Help-seeking
attitudes (IASMHS)

Time 3 .41 [-.84, 1.66] .40 [-.47, 1.27] 2.60 [-1.59, 6.80] .16 [-.74, 1.02]
Time 4 .54 [-.89, 1.97] 1.11 [-.45, 2.68] .30 [-5.72, 6.33] .58 [-.85, 2.79]
Help-seeking
intentions (GHSQ)

Time 3 .62 [-.81, 2.04] -.22 [-.86, .41] 1.23 [-1.69, 4.14] -.15 [-1.03, .32]
Time 4 .54 [-.70, 1.97] .01 [-.75, .78] .80 [-2.33, 3.93] -.02 [-.84, .62]
Depressive
symptoms (PHQ-9)

Time 3 .55 [-.81, 1.92] -.34 [-.67, -.03]* .98 [-.51, 2.49] -.19 [-.87, .25]
Time 4 .54 [-.89, 1.97] -.69 [-1.26, -.12] .53 [-1.65, 2.70] -.37 [-1.30, .52]
Note: Results are effect coefficients and confidence intervals. * denotes a significance value of p
< .05.

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Moderated Mediation analyses
Table 9

Condition x ethnicity mediation models (mediator = personal MHS)

Outcome variable Effect of
interaction on
mediator (a)
Effect of mediator
on DV (b)
Index of moderated
mediation
Help-seeking
attitudes (IASMHS)

Time 3 -2.32 [-5.68, 1.04] 1.17 [-11.79, 14.16] -2.96 [-10.74, 1.26]
Time 4 9.03 [-9.69, 24.76] 4.35 [-15.72, 24.43] -1.69 [-8.31, 3.27]
Help-seeking
intentions (GHSQ)

Time 3 3.88 [-3.57, 11.32] 5.45 [-2.39, 13.29] .47 [-1.39, 3.64]
Time 4 -.99 [-9.86, 7.89] 2.11 [-7.85, 12.07] .05 [-1.46, 3.07]
Depressive
symptoms (PHQ-9)

Time 3 2.79 [-3.48, 4.04] .06 [-4.17, 4.29] .82 [-.50, 2.89]
Time 4 -1.32 [-7.45, 4.80] 3.02 [-4.52, 10.56] .99 [-1.73, 5.79]

Note: Results are effect coefficients and confidence intervals.

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Table 10

Condition x ethnicity mediation models (mediator = societal MHS)

Outcome variable Effect of interaction
on mediator (a)
Effect of mediator
on DV (b)
Index of moderated
mediation
Help-seeking
attitudes (IASMHS)

Time 3 -2.83 [-8.44, .14.53] 9.17 [-4.98, 23.32] 2.35 [-6.24, 8.13]
Time 4 -3.85 [-7.58, 4.94] 10.66 [-8.82, 30.15] -2.30 [-13.41, 4.65]
Help-seeking
intentions (GHSQ)

Time 3 -2.83 [-6.56, .89] 5.34 [-2.61, 13.28] .34 [-1.75, 3.32]
Time 4 -1.78 [-6.51, 2.94] 2.22 [-7.50, 11.93] -.09 [-3.77, 3.66]
Depressive
symptoms (PHQ-9)

Time 3 -2.83 [-6.56, .89] -1.29 [-5.26, 2.77] 1.06 [-.54, 2.75]
Time 4 -1.78 [-6.51, 2.94] -.48 [-7.85, 6.88] 1.23 [-2.78, 8.09]

Note: Results are effect coefficients and confidence intervals.
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Table 11

Condition x interdependence mediation models (mediator = personal MHS)

Outcome variable Effect of interaction
on mediator (a)
Effect of mediator
on DV (b)
Index of moderated
mediation
Help-seeking
attitudes (IASMHS)

Time 3 -.45 [-1.31, .41] .42 [.04, .81] .01 [-.24, .21]
Time 4 -.03 [-.18, .12] .45 [-.001, .90] -.05 [-.48, .19]
Help-seeking
intentions (GHSQ)

Time 3 .02 [-.11, .15] .10 [-.13, .33] .003 [-.06, .06]
Time 4 -.02 [-.17, .14] .21 [-.03, .46] -.001 [-.07, .04]
Depressive symptoms
(PHQ-9)

Time 3 .04 [-.10, .19] -.01 [-.14, .12] .01 [-.04, .07]
Time 4 .07 [-.13, .27] -.03 [-.22, .16] -.01 [-.12, .10]

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Table 12

Interdependence x condition mediation models (mediator = societal MHS)

Outcome variable Effect of interaction
on mediator (a)
Effect of mediator
on DV (b)
Index of moderated
mediation
Help-seeking
attitudes (IASMHS)

Time 3 .72 [-.27, 1.15] .42 [.04, .81]* -.13 [-.39, .05]
Time 4 .57 [.08, 1.06]* .45 [-.001, .90] -.07 [-.26, .08]
Help-seeking
intentions (GHSQ)

Time 3 -.09 [-.22, .03] .11 [-.13, .34] .02 [-.05, .09]
Time 4 -.07 [-.18, .04] .21 [-.04, .46] .0004 [-.08, .07]
Depressive symptoms
(PHQ-9)

Time 3 -.91 [-.21, .03] -.01 [-.13, .12] .03 [-.02, .08]
Time 4 -.07 [-.18, .04] -.03 [-.21, .16] .06 [-.06, .16]
Note: Results are effect coefficients and confidence intervals. * denotes a significance value of p
< .05.

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Figures
Figure 1

Flow chart of participant recruitment, randomization, follow-up, and analysis

Assessed for eligibility (n = 1,522)
• From subject pool (n = 1,152)
• From flyers/advertisements (n = 370)

Excluded (n = 1,447)
• Did not meet inclusion criteria
(n = 1310)
• Declined to participate
(n = 137)

Randomized; stimuli created (n = 75)

Recruitment/Screening (Time 1)
Psychoeducation only (n = 19)
• No-show (n = 0)
• Failed manipulation check (n = 1)

Placebo Control (n = 21)
• No-show (n = 2)
• Failed manipulation check (n = 0)

Total N = 61
• Combined (n = 24)
• Psychoeducation (n = 18)
• Placebo-Control (n = 19)
2-week Follow-up (Time 3)
Lost at 2-week follow up
• Combined (n = 3)
• Psychoeducation (n = 4)
• Placebo control (n = 6)
Lost at 3-month follow up
• Combined (n = 3)
• Psychoeducation (n = 5)
• Placebo control (n = 3)

3-month Follow-up (Time 4)
Post-Intervention (Time 2)
Combined (n = 35)
• No-show (n = 7)
• Failed manipulation check (n = 4)

Social Norms Only (n = 14)
• No-show (n = 4)
• Failed manipulation check (n = 2)

Social Norms + Psychoeducation (n = 21)
• No-show (n = 3)
• Failed manipulation check (n = 2)

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Figure 2.
Changes in mental health outcome variables over time

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64

Figure 3
Interaction between condition and interdependence on trajectory of mental health service
utilization rates over time.

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65

Figure 4
Interaction between condition and interdependence on trajectory of personal mental health
stigma over time.

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Appendix A

Sample Stimuli
Sample PNF Slides

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Sample Psychoeducation Slides

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Sample Control Slides

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Appendix B
Measures
Social Norms Questions
Peer Norms
Indicate the extent to which you agree with the following statements (1=strongly disagree to
7=strongly agree).

1. I always do my best to behave inclusively towards those with mental illness
2. I think it is shameful to see a therapist for mental health problems.
3. I would react negatively towards someone who said or did something discriminatory
against those with mental illness
4. I am welcoming and accommodating towards those with mental illness
5. I am supportive and accepting of my peers who utilize psychological treatment for their
mental health problems
6. Do you think that seeking psychological treatment for mental health problems is a good
thing? (Yes/No)

Perceptions of Peer Norms
(Slider scale from 0-100%)
1. What percentage of USC students would behave in an inclusive manner towards those
with mental illness?
2. What percentage of USC students would think that it is shameful to see a therapist for
mental health problems?
3. What percentage of USC students would react negatively if you did or said something
discriminatory against someone with a mental illness?
4. What percentage of USC students are welcoming and accommodating towards those with
mental illness?
5. What percentage of USC students would be supportive and accepting of peers who utilize
psychological treatment for their mental health problems?
6. What percentage of your peers think that seeking psychological treatment for mental
health problems is a good thing?

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Control Norms Questions
Control Peer Norms

On average, how many hours a day would you estimate that you spend on the following
activities?
(slider scale from 0-24 hours)

1. Texting
2. Browsing social media (e.g., Instagram, Facebook, etc.)
3. Listening to music
4. Playing video games

Control Perceptions of Peer Norms

On average, how many hours a day would you estimate that your peers spend on the following
activities?
(slider scale from 0-24 hours)

1. Texting
2. Browsing social media (e.g., Instagram, Facebook, etc.)
3. Listening to music
4. Playing video games

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Patient Health Questionnaire (PHQ-9)

Instructions: Over the last 2 weeks, how often have you been bothered by any of the following
problems?

0 = Not at all
1 = Several days
2 = More than half the days
3 = Nearly every day
____1. Little interest or pleasure in doing things
____2. Feeling down, depressed, or hopeless
____3. Trouble falling or staying asleep, or sleeping too much
____4. Feeling tired or having little energy
____5. Poor appetite or overeating
____6. Feeling bad about yourself – or that you are a failure or have let yourself or your family
down
____7. Trouble concentrating on things, such as reading the newspaper or watching television
____8. Moving or speaking so slowly that other people could have noticed. Or the opposite—
being so fidgety or restless that you have been moving around a lot more than usual
____9. Thoughts that you would be better off dead, or of hurting yourself

10. If you checked off any problems, how difficult have these
problems made it for you to do your work, take care of things at
home, or get along with other people?
0 = Not difficult at all
1 = Somewhat difficult
2 = Very difficult
3 = Extremely difficult

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Singelis Self-Construal Scale
Instructions:
This is a questionnaire that measures a variety of feelings and behaviors in various situations.
Listed below are a number of statements. Read each one as if it referred to you. Beside each
statement write the number that best matches your agreement or disagreement. Please respond to
every statement. Thank you.
1=STRONGLY DISAGREE 2=DISAGREE 3=SOMEWHAT DISAGREE 4=DON’T AGREE
OR DISAGREE 5=AGREE SOMEWHAT 6=AGREE 7=STRONGLY AGREE
____1. I enjoy being unique and different from others in many respects.
____2. I can talk openly with a person who I meet for the first time, even when this person is
much older than I am.

____3. Even when I strongly disagree with group members, I avoid an argument.
____4. I have respect for the authority figures with whom I interact.
____5. I do my own thing, regardless of what others think.

____6. I respect people who are modest about themselves.

____7. I feel it is important for me to act as an independent person.
____8. I will sacrifice my self interest for the benefit of the group I am in.
____9. I'd rather say "No" directly, than risk being misunderstood.
____10. Having a lively imagination is important to me.
____11. I should take into consideration my parents' advice when making education/career plans.

____12. I feel my fate is intertwined with the fate of those around me.
____13. I prefer to be direct and forthright when dealing with people I've just met.
____14. I feel good when I cooperate with others.

____15. I am comfortable with being singled out for praise or rewards.
____16. If my brother or sister fails, I feel responsible.

____17. I often have the feeling that my relationships with others are more important than my
own accomplishments.
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____18. Speaking up during a class (or a meeting) is not a problem for me.

____19. I would offer my seat in a bus to my professor (or my boss).
____20. I act the same way no matter who I am with.

____21. My happiness depends on the happiness of those around me.
____22. I value being in good health above everything.

____23. I will stay in a group if they need me, even when I am not happy with the group.
____24. I try to do what is best for me, regardless of how that might affect others.

____25. Being able to take care of myself is a primary concern for me.
____26. It is important to me to respect decisions made by the group.
____27. My personal identity, independent of others, is very important to me.

____28. It is important for me to maintain harmony within my group.
____29. I act the same way at home that I do at school (or work).
____30. I usually go along with what others want to do, even when I would rather do something
different.

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Social Distancing Scale
Personal Social Distancing
Instructions: Please answer the questions below, indicating the extent of your willingness or
unwillingness to engage in the scenarios described, using the following scale:

1 = Definitely Willing
2 = Probably Willing
3 = Probably Unwilling
4 = Definitely Unwilling
_____ 1. How would you feel about renting your home to a person with a mental illness?
_____ 2. How would you feel about working with a person with a mental illness?
_____ 3. How would you feel about having a person with a mental illness as your neighbor?
_____ 4. How would you feel about having a person with a mental illness as the caretaker of
your own children?
_____ 5. How would you feel about having your children marry a person with a mental illness?
_____ 6. How would you feel about introducing a person with a mental illness to your friends?
_____ 7. How would you feel about recommending a person with a mental illness for a job
working with someone you know?
Societal Social Distancing
Instructions: Please answer the questions below, indicating the extent of most people’s
willingness or unwillingness to engage in the scenarios described, using the following scale:
1 = Definitely Willing
2 = Probably Willing
3 = Probably Unwilling
4 = Definitely Unwilling
_____ 1. How would most people feel about renting their home to a person with a mental illness?
_____ 2. How would most people feel about working with a person with a mental illness?
_____ 3. How would most people feel about having a person with a mental illness as their
neighbor?
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_____ 4. How would most people feel about having a person with a mental illness as the
caretaker of their own children?
_____ 5. How would most people feel about having their children marry a person with a mental
illness?
_____ 6. How would most people feel about introducing a person with a mental illness to their
friends?
_____ 7. How would most people feel about recommending a person with a mental illness for a
job working with someone they know?

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General Help-Seeking Questionnaire
Instructions: If you were having a personal or emotional problem, how likely is it that you
would seek help from the following people?
1= Extremely Unlikely 3 = Unlikely 5 = Likely 7 = Extremely Unlikely
_____ 1. Intimate partner (e.g., girlfriend, boyfriend, husband, wife, de’facto)
_____ 2. Friend (not related to you)
_____ 3. Parent
_____ 4. Other relative/family member
_____ 5. Mental health professional (e.g., psychologist, social worker, counsellor)
_____ 6. Phone helpline (e.g., Lifeline)
_____ 7. Doctor/General Practitioner
_____ 8. Minister or religious leader (e.g., Priest, Rabbi, Chaplain)
_____ 9. I would not seek help from anyone
_____ 10. I would seek help from another not listed above (please list in the space provided, e.g.,
work colleague. If no, leave blank) ___________________________________________

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Inventory of Attitudes Towards Mental Health Services (IASMHS)

The term professional refers to individuals who have been trained to deal with mental health
problems (e.g., psychologists, psychiatrists, social workers, and family physicians). The term
psychological problems refers to reasons one might visit a professional. Similar terms include
mental health concerns, emotional problems, mental troubles, and personal difficulties.

For each item, indicate whether you disagree (0), somewhat disagree (l), are undecided (2),
somewhat agree (3), or agree (4):
_____ 1. There are certain problems which should not be discussed outside of one’s immediate
family.
_____ 2. I would have a very good idea of what to do and who to talk to if I decided to seek
professional help for psychological problems.
_____ 3. I would not want my significant other (spouse, partner, etc.) to know if I were suffering
from psychological problems.
_____ 4. Keeping one’s mind on a job is a good solution for avoiding personal worries and
concerns.
_____ 5. If good friends asked my advice about a psychological problem, I might recommend
that they see a professional.
_____ 6. Having been mentally ill carries with it a burden of shame.
_____ 7. It is probably best not to know everything about oneself.
_____ 8. If I were experiencing a serious psychological problem at this point in my life, I would
be confident that I could find relief in psychotherapy.
_____ 9. People should work out their own problems; getting professional help should be a last
resort.
_____ 10. If I were to experience psychological problems, I could get professional help if I
wanted to.
_____ 11. Important people in my life would think less of me if they were to find out that I was
experiencing psychological problems.
_____ 12. Psychological problems, like many things, tend to work out by themselves.
_____ 13. It would be relatively easy for me to find the time to see a professional for
psychological problems.
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_____ 14. There are experiences in my life I would not discuss with anyone.
_____ 15. I would want to get professional help if I were worried or upset for a long period of
time.
_____ 16. I would be uncomfortable seeking professional help for psychological problems
because people in my social or business circles might find out about it.
_____ 17. Having been diagnosed with a mental disorder is a blot on a person’s life.
_____ 18. There is something admirable in the attitude of people who are willing to cope with
their conflicts and fears without resorting to professional help.
_____ 19. If I believed I were having a mental breakdown, my first inclination would be to get
professional attention.
_____ 20. I would feel uneasy going to a professional because of what some people would think.
_____ 21. People with strong characters can get over psychological problems by themselves and
would have little need for professional help.
_____ 22. I would willingly confide intimate matters to an appropriate person if I thought it
might help me or a member of my family.
_____ 23. Had I received treatment for psychological problems, I would not feel that it ought to
be “covered up.”
_____ 24. I would be embarrassed if my neighbor saw me going into the office of a professional
who deals with psychological problems.

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Treatment Inventory of Costs in Patients with Psychiatric Disorders (TiC-P)
Over the past 2 weeks (3 months) have you used any of the following sources for any emotional
or mental health problems? Please select all that apply.
_____ 1. General practitioner (i.e., primary care doctor)
_____ 2. Health care professional from USC’s Engemann Student Health Center or the Eric
Cohen Student Health Center.
_____ 3. Psychiatrist, psychologist, or psychotherapist in private practice
_____ 4. Psychiatrist, psychologist, or psychotherapist outpatient care
_____ 5. Occupational physician
_____ 6. Medical specialist at the outpatient care (examples of medical specialists are
cardiologists, rheumatologists, internists, or neurologists)
_____ 7. “Let’s Talk” and other drop-in programs to talk to a counselor
_____ 8. Social worker
_____ 9. Clinic for alcohol and drugs or similar institution
_____ 10. Telehealth (i.e., videochat sessions with a mental health provider)
_____ 11. Alternative medicine (examples of alternative medicine include homeopaths and
acupuncturists)
_____ 12. Psychiatric day care
_____ 13. Inpatient care (i.e., admission to, e.g., a hospital, a revalidation center, or a psychiatric
hospital)
_____ 14. Self-help group (e.g., the Alcoholics Anonymous group, support group with a patient
association)
_____ 15. Medication
_____16. Therapy app
_____17. Mental health intake for a referral
_____18. Other (please specify)
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Appendix C
Analyses with combined social norms conditions separated
Main Effect of Condition
No significant difference between the combined, social norms intervention,
psychoeducation, or placebo control in trajectories of personal or societal mental health stigma,
help-seeking attitudes or intentions, service utilization, or depression over time, p > .05.
Interaction Effects
Ethnicity did not significantly moderate condition effects for any of the mental health
outcomes over time. Interdependence significantly moderated trajectories of service utilization,
such that those in the combined condition had the highest rates of service utilization if they were
more interdependent (vs. less interdependent), followed by those in the social norms condition
only, then psychoeducation only, and finally, placebo control. In addition, there was a marginally
significant interaction between condition and interdependence on personal mental health stigma,
p = .09. The two social norms conditions (vs. psychoeducation and placebo control) appeared to
be associated with decreased stigma over time for those with higher levels of interdependence
than lower interdependence.
Mediation and Moderated Mediation Effects
Personal and societal mental health stigma did not mediate condition effects on any of the
mental health outcomes at Times 3 and 4. In addition, ethnicity and interdependence did not
significantly moderate the mediating effects of personal and societal mental health stigma during
those time periods, p > .05.

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Asset Metadata

Creator Wang, Crystal Xiaolu (author)

Core Title Social norms intervention to promote help-seeking with depressed Asian and European Americans: A pilot study

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Degree Conferral Date 2022-08

Publication Date 07/26/2022

Defense Date 06/02/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Asian American,college student mental health,culture,help-seeking intervention,interdependence,mental health disparities,mental health stigma,OAI-PMH Harvest,social norms

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Huey, Stanley Jr. (committee chair), Jang, Yuri (committee member), Lopez, Steven (committee member), Oyserman, Daphna (committee member), Yamada, Ann Marie (committee member)

Creator Email crystaxw@usc.edu,crywang93@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111375231

Unique identifier UC111375231

Legacy Identifier etd-WangCrysta-10988

Document Type Dissertation

Format application/pdf (imt)

Rights Wang, Crystal Xiaolu

Type texts

Source 20220728-usctheses-batch-962 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu